Tagging Sentence Boundaries
(2000)

In this paper we tackle sentence boundary disambiguation through a part-of-speech (POS) tagging framework. We describe necessary changes in text tokenization and the implementat ion of a POS tagger and provide results of an evaluation of this system on two corpora. We also describe an extension of the traditional POS tagging by combining it with the document-centered approach to proper name identification and abbreviation handling. This made the resulting system robust to domain and topic shifts. 1 I n t r o d u c t i o n Sentence boundary disambiguation (SBD) is an important aspect in developing virtually any practical text processing application syntactic parsing, Information Extraction, Machine Translation, Text Alignment, Document Summarization, etc. Segmenting text into sentences in most cases is a simple m a t t e r a period, an exclamation mark or a question mark usually signal a sentence boundary. However, there are cases when a period denotes a decimal point or is a part of an abbreviation and thus it does not signal a sentence break. Furthermore, an abbreviation itself can be the last token in a sentence, in which case its period acts at the same time as part of this abbreviation and as the end-of-sentence indicator (fullstop). The first large class of sentence boundary disambiguators uses manually built rules which are usually encoded in terms of regular expression grammars supplemented with lists of abbreviations, common words, proper names, etc. For instance, the Alembic workbench (Aberdeen et al., 1995) contains a sentence splitting module which employs over 100 regular-expression rules written in Flex. To put together a few rules which do a job is fast and easy, but to develop a good rule-based system is quite a labour consuming enterprise. Another potential shortcoming is that such systems are usually closely tailored to a particular corpus and are not easily portable across domains. Automatically trainable software is generally seen as a way of producing systems quickly re-trainable for a new corpus, domain or even for another language. Thus, the second class of SBD systems employs machine learning techniques such as decision tree classifiers (Riley, 1989), maximum entropy modeling (MAXTERMINATOR) (Reynar and Ratnaparkhi, 1997), neural networks (SATZ) (Palmer and Hearst , 1997), etc.. Machine learning systems treat the SBD task as a classification problem, using features such as word spelling, capitalization, suffix, word class, etc., found in the local context of potent im sentence breaking punctuation. There is, however, one catch all machine learning approaches to the SBD task known to us require labeled examples for training. This implies an investment in the annotat ion phase. There are two corpora normally used for evaluation and development in a number of text processing tasks and in the SBD task in particular: the Brown Corpus and the Wall Street Journal (WSJ) corpus both par t of the Penn Treebank (Marcus, Marcinkiewicz, and Santorini, 1993). Words in both these corpora are annota ted with part-ofspeech (POS) information and the text is split into documents, paragraphs and sentences. This gives all necessary information for the development of an SBD system and its evaluation. State-of-theart machine-learning and rule-based SBD systems achieve the error rate of about 0.8-1.5% measured on the Brown Corpus and the WSJ. The best performance on the WSJ was achieved by a combination of the SATZ system with the Alembic system 0.5% error rate. The best performance on the Brown Corpus, 0.2% error rate, was reported by (Riley, 1989), who trained a decision tree classifier on a 25 million word corpus. 1.1 W o r d b a s e d vs . S y n t a c t i c M e t h o d s The first source of ambiguity in end-of-sentence marking is introduced by abbreviations: if we know tha t the word which precedes a period is n o t an abbreviation, then almost certainly this period denotes a sentence break. However, if this word is an abbreviation, then it is not tha t easy to make a clear decision. The second major source of information